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Extreme value theory is a book category focused on the probability and statistics of rare, high-impact events, emphasizing how to model maxima, minima, and the tails of distributions beyond ordinary variability. Titles in this genre cover the theoretical foundations (e.g., domains of attraction), core models such as the generalized extreme value and generalized Pareto distributions, and practical frameworks like block maxima and peaks-over-threshold, including threshold selection, tail index estimation, and return levels/periods. Many works address dependence and clustering, multivariate and spatial extremes, point-process formulations, heavy tails and regular variation, and the treatment of nonstationarity and covariates. Methodology typically spans likelihood and Bayesian inference, diagnostics, uncertainty quantification, and resampling, often with reproducible case studies and software in R or Python. Applications include climate and hydrology, insurance and finance, engineering reliability, and environmental and infrastructure risk, making the category suitable for graduate students and practitioners seeking rigorous yet applied tools for modeling rare events.

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